Genetic crossovers are predicted accurately by the computed human recombination map.
Genetic crossovers are predicted accurately by the computed human recombination map.
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DOI：
10.1371/journal.pgen.1000831
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发表时间：
2010-01-29
期刊：
影响因子：
4.5
通讯作者：
Camerini-Otero RD
中科院分区：
文献类型：
--
作者：
Khil PP;Camerini-Otero RD
减数分裂重组的热点可以随时间迅速变化。这种不稳定性和所报道的减数分裂重组中的高水平个体间变异使计算的热点图的准确性受到质疑，该热点图是基于过去遗传交叉的总和。为了估计计算的重组率地图的准确性，我们已经映射了遗传交叉的中位数分辨率为70 Kb的10 CEPH家系。然后，我们将交叉的位置与从HapMap数据计算的热点进行了比较，并进行了广泛的计算机模拟，以将观察到的交叉分布与从计算的重组率图中预期的分布进行比较。在这里，我们表明，从连锁不平衡数据计算的人口平均热点图预测以及现今的遗传交叉。我们发现，计算热点图准确估计的强度和减数分裂热点的位置。对未预测的交叉的深入研究表明，它们优先位于其他人群中发现热点的区域。总之，我们发现，通过结合几个计算出的群体特异性图谱，我们可以捕获单个热点的变化，以生成一个热点图谱，该图谱可以预测几乎所有的当今遗传交叉。在真核生物中，遗传交换负责产生遗传多样性并确保染色体的正确分离。基因交叉紧密地聚集在热点区域。虽然热点在人类中的存在已被明确证明，但其形成机制和减数分裂重组的调控一般仍知之甚少。减数分裂重组研究中的另一个复杂因素是，用目前的方法在全基因组范围内对人类热点进行直接的实验定位是不可行的。最好的间接方法是根据群体样本中遗传标记之间的历史关联模式来计算热点的位置。在这项研究中，我们确定了10个欧洲血统的家系中的遗传交叉的位置，然后将交叉的位置与HapMap数据计算的热点进行比较。重要的是，我们发现，人口平均计算的地图是在密切的遗传交叉观察到的分布。我们还发现，不容易检测到的计算欧洲地图中的神秘热点，可以更有效地确定，如果其他人口包括在分析中。我们的分析表明，高分辨率的重组配置文件是高度相似的远亲群体之间，包括计算热点从几个人口，我们可以预测几乎所有的交叉。
Hotspots of meiotic recombination can change rapidly over time. This instability and the reported high level of inter-individual variation in meiotic recombination puts in question the accuracy of the calculated hotspot map, which is based on the summation of past genetic crossovers. To estimate the accuracy of the computed recombination rate map, we have mapped genetic crossovers to a median resolution of 70 Kb in 10 CEPH pedigrees. We then compared the positions of crossovers with the hotspots computed from HapMap data and performed extensive computer simulations to compare the observed distributions of crossovers with the distributions expected from the calculated recombination rate maps. Here we show that a population-averaged hotspot map computed from linkage disequilibrium data predicts well present-day genetic crossovers. We find that computed hotspot maps accurately estimate both the strength and the position of meiotic hotspots. An in-depth examination of not-predicted crossovers shows that they are preferentially located in regions where hotspots are found in other populations. In summary, we find that by combining several computed population-specific maps we can capture the variation in individual hotspots to generate a hotspot map that can predict almost all present-day genetic crossovers. In eukaryotes genetic crossovers are responsible for generating genetic diversity and ensuring the proper segregation of chromosomes. Genetic crossovers are tightly clustered in hotspots. Although the existence of hotspots in humans is clearly proven, mechanisms of their formation and the regulation of meiotic recombination in general remain poorly understood. An additional complication in studies of meiotic recombination is the fact that the direct experimental mapping of human hotspots on a genome-wide scale is not feasible with current methods. The best available indirect methods compute the position of hotspots from patterns of historic associations between genetic markers in population samples. In this study we determined the positions of genetic crossovers in ten pedigrees of European origin and then compared the positions of crossovers with the hotspots computed from HapMap data. Importantly, we find that the population-averaged computed map is in close agreement with the observed distribution of genetic crossovers. We also find that cryptic hotspots that are not easily detected in the computed European map can be more effectively identified if other populations are included in the analysis. Our analysis shows that high-resolution recombination profiles are highly similar between distantly related populations and that by including computed hotspots from several populations we can predict nearly all crossovers.
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影响因子：
64.8
作者：
Frazer, Kelly A.;Ballinger, Dennis G.;Cox, David R.;Hinds, David A.;Stuve, Laura L.;Gibbs, Richard A.;Belmont, John W.;Boudreau, Andrew;Hardenbol, Paul;Leal, Suzanne M.;Pasternak, Shiran;Wheeler, David A.;Willis, Thomas D.;Yu, Fuli;Yang, Huanming;Zeng, Changqing;Gao, Yang;Hu, Haoran;Hu, Weitao;Li, Chaohua;Lin, Wei;Liu, Siqi;Pan, Hao;Tang, Xiaoli;Wang, Jian;Wang, Wei;Yu, Jun;Zhang, Bo;Zhang, Qingrun;Zhao, Hongbin;Zhao, Hui;Zhou, Jun;Gabriel, Stacey B.;Barry, Rachel;Blumenstiel, Brendan;Camargo, Amy;Defelice, Matthew;Faggart, Maura;Goyette, Mary;Gupta, Supriya;Moore, Jamie;Nguyen, Huy;Onofrio, Robert C.;Parkin, Melissa;Roy, Jessica;Stahl, Erich;Winchester, Ellen;Ziaugra, Liuda;Altshuler, David;Shen, Yan;Yao, Zhijian;Huang, Wei;Chu, Xun;He, Yungang;Jin, Li;Liu, Yangfan;Shen, Yayun;Sun, Weiwei;Wang, Haifeng;Wang, Yi;Wang, Ying;Xiong, Xiaoyan;Xu, Liang;Waye, Mary M. Y.;Tsui, Stephen K. W.;Wong, J. Tze-Fei;Galver, Luana M.;Fan, Jian-Bing;Gunderson, Kevin;Murray, Sarah S.;Oliphant, Arnold R.;Chee, Mark S.;Montpetit, Alexandre;Chagnon, Fanny;Ferretti, Vincent;Leboeuf, Martin;Olivier, Jean-Franccois;Phillips, Michael S.;Roumy, Stephanie;Sallee, Clementine;Verner, Andrei;Hudson, Thomas J.;Kwok, Pui-Yan;Cai, Dongmei;Koboldt, Daniel C.;Miller, Raymond D.;Pawlikowska, Ludmila;Taillon-Miller, Patricia;Xiao, Ming;Tsui, Lap-Chee;Mak, William;Song, You Qiang;Tam, Paul K. H.;Nakamura, Yusuke;Kawaguchi, Takahisa;Kitamoto, Takuya;Morizono, Takashi;Nagashima, Atsushi;Ohnishi, Yozo;Sekine, Akihiro;Tanaka, Toshihiro;Tsunoda, Tatsuhiko;Deloukas, Panos;Bird, Christine P.;Delgado, Marcos;Dermitzakis, Emmanouil T.;Gwilliam, Rhian;Hunt, Sarah;Morrison, Jonathan;Powell, Don;Stranger, Barbara E.;Whittaker, Pamela;Bentley, David R.;Daly, Mark J.;de Bakker, Paul I. W.;Barrett, Jeff;Chretien, Yves R.;Maller, Julian;McCarroll, Steve;Patterson, Nick;Pe'er, Itsik;Price, Alkes;Purcell, Shaun;Richter, Daniel J.;Sabeti, Pardis;Saxena, Richa;Schaffner, Stephen F.;Sham, Pak C.;Varilly, Patrick;Altshuler, David;Stein, Lincoln D.;Krishnan, Lalitha;Smith, Albert Vernon;Tello-Ruiz, Marcela K.;Thorisson, Gudmundur A.;Chakravarti, Aravinda;Chen, Peter E.;Cutler, David J.;Kashuk, Carl S.;Lin, Shin;Abecasis, Goncalo R.;Guan, Weihua;Li, Yun;Munro, Heather M.;Qin, Zhaohui Steve;Thomas, Daryl J.;McVean, Gilean;Auton, Adam;Bottolo, Leonardo;Cardin, Niall;Eyheramendy, Susana;Freeman, Colin;Marchini, Jonathan;Myers, Simon;Spencer, Chris;Stephens, Matthew;Donnelly, Peter;Cardon, Lon R.;Clarke, Geraldine;Evans, David M.;Morris, Andrew P.;Weir, Bruce S.;Tsunoda, Tatsuhiko;Johnson, Todd A.;Mullikin, James C.;Sherry, Stephen T.;Feolo, Michael;Skol, Andrew
通讯作者：
Skol, Andrew
影响因子：
16
作者：
Jeffreys, AJ;Murray, J;Neumann, R
通讯作者：
Neumann, R
影响因子：
9.8
作者：
Fearnhead, P;Smith, NGC
通讯作者：
Smith, NGC
影响因子：
7
作者：
Greenawalt, DM;Cui, XF;Li, HH
通讯作者：
Li, HH
影响因子：
30.8
作者：
Jeffreys, AJ;Neumann, R
通讯作者：
Neumann, R